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Record W2478936755 · doi:10.1139/cjce-2016-0052

Volume change behaviour and microstructure of stabilized loess under cyclic freeze–thaw conditions

2016· article· en· W2478936755 on OpenAlexaffvenue
Shenglin Wang, Qingfeng Lv, Hassan Baaj, Xiaoyuan Li, Zhao Yan-xu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Waterloo
FundersNational Science Foundation
KeywordsLoessLimeMicrostructureShrinkageFly ashCementFrost (temperature)Frost heavingMaterials scienceVolume (thermodynamics)Geotechnical engineeringPorosityComposite materialGeologyMetallurgyGeomorphology

Abstract

fetched live from OpenAlex

Freeze–thaw action is considered to be one of the most destructive actions that can induce significant damage in stabilized subgrades in seasonally frozen loess areas. Laboratory tests including frost heave – thaw shrinkage and microstructure change during freeze–thaw cycles were conducted to evaluate the volume change rate of loess stabilized with cement, lime, and fly ash under the impact of cyclic freeze–thaw conditions. The loess specimens collapsed after eight freeze–thaw cycles (192 h), but most stabilized loess specimens had no visible damage after all freeze–thaw cycles were completed. All of the stabilized loess samples underwent a much smaller volume change than the loess alone after the freeze–thaw cycles. Although surface porosity and equivalent diameter of stabilized loess samples increased, the stabilized loess can retain its microstructure during freeze–thaw cycles when the cement content was less than 6%. To ensure freeze–thaw resistance of stabilized loess subgrades, the mix proportions of the three additives was recommended to be 4 to 5% cement, 6% lime, and 10% fly ash.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.202
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations75
Published2016
Admission routes2
Has abstractyes

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